Why Will SQL Always Be the Data Scientist's Best Friend?

Data science and engineering are thriving, competitive fields. The point of disagreement between data scientists and engineers is over which skills will be most in demand in the future. All ambitious data professionals agree that it's important to keep your skill set up-to-date because technologies change frequently.

Despite the dizzying assortment of tools and services accessible to data scientists, basic SQL continues to serve as the foundation of their toolkit. Although SQL is frequently thought of as a foundational skill, it is actually much more than that. SQL is still useful today despite being close to 50 years old.

While big data analytics, AI, and machine learning may grab the spotlight, SQL is still the best way to develop a clever, strategic talent that will advance your career.

Here's Why:

SQL Dominates Databases

It is simple to understand why everyone who needs to query, update, edit, or otherwise interact with data in relational databases will benefit from having a strong working knowledge of SQL, regardless of the specialty they choose to pursue. First of all, according to Benjamin Rogojan, "SQL is actually the language of data" (aka Seattle Data Guy). This is because most databases are created using one of the SQL-based technologies or another. Except for Redis and MongoDB, which are placed fifth and sixth, all of the top 10 most common databases in use today are based on SQL. Even these exceptions can be used with SQL.

Demand for SQL Skills is High and Growing

SQL is far from a legacy talent, despite its antiquity. Dataquest reports that SQL was the most in-demand talent across all data positions in 2021, particularly at the more entry-level end of the spectrum, as data engineering has gone into the cloud. Even still, nearly 60% of job listings for data scientists with more experience still mention SQL. Additionally, despite a small decline in demand in 2021, the demand for SQL abilities appears to be increasing, probably due to the soaring demand for data-related talent. Leaving the pandemic aside, it is anticipated that the market for SQL server transformation, which aids businesses in meeting their demand for data transformation, will expand gradually at a CAGR of more than 10% until the end of the decade.

Should Savvy Data Scientists Prioritize SQL?

Although SQL's future appears secure, it does not necessarily follow those aspiring data scientists who currently have a solid understanding of it. They will give developing their SQL abilities priority to advance their careers.

Data scientists need to know where to focus their efforts given the abundance of tools and emerging technologies available to assist them at the ELT/ETL stage, for BI, and for both predictive and historical analytics. Because high-tech skills have a constantly decreasing half-life, data scientists need specialized equipment and expertise.

How is SQL Taking Center Stage?

Nobody wants to spend six months learning a technology that only meets half of their expectations, let alone advocate it to the rest of the team, only to disappoint them. Consequently, when data scientists consider the tools and methods available to them for more effective data querying,

They will likely examine the best BI tools and ML extensions in order to prepare the data, build the model, and train it. But each of these phases requires a lot of expertise and time. The data must first be taken from the database, often using a BI tool, then modified and fed into the BI system before being exported (again) to the ML tool, where the magic happens, then sent back to the BI tool for visualization. In a , all of these processes will be discussed elaborately.

In-database Innovation

Using in-database ML makes it much easier to use current data to forecast future events. And it employs common SQL commands. Giving your database a brain with in-database ML is similar to that. It implies that anyone with SQL expertise can operate within the database, running ML models to address nearly any business topic. This includes data scientists, data engineers, and anyone else. A few of the numerous applications that in-database modeling has made possible include predicting customer attrition, credit scoring, customer lifecycle optimization, fraud detection, inventory management, price modeling, and predicting patient health outcomes. With this method, all ML models can be built, queried, and maintained using SQL as if they were database tables, giving a far larger range of data and powerful prediction capabilities.

SQL – Not Old, But Evergreen

We're in the midst of a golden age of digital innovation. However, in a business environment that values the insights data can provide, data scientists are under more pressure than ever to perform data-driven miracles. The desire to scale and accelerate data analytics has led to the development of an astounding array of technologies. An investment in time and skill development is frequently necessary to fully profit from these technologies. The data scientist's best buddy, modest SQL, is one ability that is commonly overlooked. The growing trend toward more data-driven innovation demonstrates that SQL is not only here to stay but is also becoming the data scientist's tactical secret weapon.

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